Our Work

What's Changed Fix inconsistencies in MLIP-compatible recipes by @Andrew-S-Rosen in https://github.com/Quantum-Accelerators/quacc/pull/3495 Split odac_static_job into odac23_static_job and odac25_sta…

What's Changed Fix inconsistencies in MLIP-compatible recipes by @Andrew-S-Rosen in https://github.com/Quantum-Accelerators/quacc/pull/3495 Split odac_static_job into odac23_static_job and odac25_static_job by @Andrew-S-Rosen in https://github.com/Quantum-Accelerators/quacc/pull/3496 Follow-up fixes for MLIP-compatible recipes by @Andrew-S-Rosen in https://github.com/Quantum-Accelerators/quacc/pull/3497 Link to the peer-reviewed ODAC23 paper by @Andrew-S-Rosen in https://github.com/Quantum-Accelerators/quacc/pull/3498 Clarify that odac25_static_job does not exactly reproduce ODAC25 labels by @Andrew-S-Rosen in https://github.com/Quantum-Accelerators/quacc/pull/3499 Full Changelog: https://github.com/Quantum-Accelerators/quacc/compare/v1.5.13...v1.6.0

paftacular is a Python implementation of the HUPO Proteomics Standards Initiative mzPAF notation for mass spectrometry peak annotations. It parses individual or comma-separated annotations into typed …

paftacular is a Python implementation of the HUPO Proteomics Standards Initiative mzPAF notation for mass spectrometry peak annotations. It parses individual or comma-separated annotations into typed PafAnnotation objects and serializes those objects back to mzPAF text. The model represents analyte references, fragment-ion series and positions, internal and precursor ions, neutral losses and gains, isotopes, adducts, charge states, mass errors, and confidence values. Parsed annotations can be inspected, constructed in code, compared, cached, and round-tripped through the canonical text form. Calculation methods derive monoisotopic or average mass and elemental composition for annotated ions using shared reference data from tacular. Optional peptacular integration connects embedded peptide sequences with ProForma parsing and fragment generation. Optional SMILES support handles formula calculations for applicable chemical structures. paftacular is intended for spectrum annotation software, spectral-library tooling, proteomics pipelines, and validation tasks that need a structured representation of mzPAF annotations. It requires Python 3.12 or later.

在回旋加速器和直线加速器里,我们知道,带电粒子的运动速度很难达到光速,只能无限接近光速运动,带电粒子在接近光速运动时,每再次提升一次速度,都需要耗费加速器很大的能量。本文通过一个铁球和小车的碰撞理想实验,作了一个带电粒子在加速器里加速的唯象解释,用以解答带电粒子在加速器里无法加速到光速的原因。我认为,超光速现象是可以在现实中存在的,质量是恒定不变的,物体的质量不会随物体的运动速度增大而增大。本文提…

在回旋加速器和直线加速器里,我们知道,带电粒子的运动速度很难达到光速,只能无限接近光速运动,带电粒子在接近光速运动时,每再次提升一次速度,都需要耗费加速器很大的能量。本文通过一个铁球和小车的碰撞理想实验,作了一个带电粒子在加速器里加速的唯象解释,用以解答带电粒子在加速器里无法加速到光速的原因。我认为,超光速现象是可以在现实中存在的,质量是恒定不变的,物体的质量不会随物体的运动速度增大而增大。本文提出了一个改进方法,利用重力和放射性衰变,就可以使带电粒子通过加速器实现超光速运动。希望人们做实验验证。 在加速器中,带电粒子的速度总是有一个上限值,只能加速到接近光速,而不能达到或者超过光速,这是为什么呢?当前的理论认为,那是因为光速是速度的极限,任何物体的运动速度都不能超过光速。并且,当前的理论认为,物体的质量不是固定不变的,而是随着物体的运动速度的增大而增大,当物体的运动速度接近光速时,物体的质量变得相当的大,物体的加速就非常的困难。当物体达到光速时,物体的质量就变得无穷大了,也就无法再加速了,所以,任何物体的运动速度都不能超过光速。 真的是这样吗? 在螺旋桨飞机水平飞行的时候,我们知道,飞机是难以达到或者超过音速的,当飞机在接近音速飞行的时候,飞机就很难以再提升速度,加速越来越困难,飞机需要花费更多的能量才能提升飞行速度。我们从来不怀疑这不是飞机的质量变大了,造成速度无法提升的原因,而是由另外的原因造成的。 我认为,物体的质量是固定不变的,不会随着物体的运动速度的增大而增大,带电粒子在加速器里以接近光速运动时,无法再提升速度的原因,不是因为质量变大了,而是由其他的因素造成的。 是什么因素造成的呢? 本文通过一个铁球和小车的碰撞理想实验,作了一个唯象解释,用以解答带电粒子在加速器里无法加速到光速的原因。以及针对这一现象,本文提出了可以使带电粒子达到或者超过光速运动的方法。

The 2021 SCOPE-PARAGON research expedition was a coordinated effort to characterize particle dynamics and remineralization in the upper 500 m of the North Pacific Subtropical Gyre. Despite the importa…

The 2021 SCOPE-PARAGON research expedition was a coordinated effort to characterize particle dynamics and remineralization in the upper 500 m of the North Pacific Subtropical Gyre. Despite the importance of particles as key sites of biological activity investigations of trace metal distributions and processes in particles especially in sinking material remain unmeasured. The 2021 SCOPE-PARAGON cruise was directed towards understanding the processes and rates of trace metals in biological particulate matter transformations (e.g., production, decomposition, sinking). These targeted studies of particle dynamics at Station ALOHA would leverage ongoing ecological and biogeochemical modeling efforts in SCOPE. 

GazeForge is vendor-neutral Python research software for auditable AI-assisted eye-tracking analysis. It provides machine-learning-assisted quality control, transparent and learned eye-event modelling…

GazeForge is vendor-neutral Python research software for auditable AI-assisted eye-tracking analysis. It provides machine-learning-assisted quality control, transparent and learned eye-event modelling, semantic and dynamic AOIs with human review, scanpath representations, participant-held-out and dataset-held-out validation, calibration diagnostics, deterministic provenance, and frozen benchmark evidence. Version 0.1.0a2 is the second public alpha release. It includes the reviewed Lund2013 external evidence suite and the provenance/rights infrastructure developed for additional external benchmarks. This alpha release does not claim native 60 Hz/GP3 device validation, completed Gaze-in-the-Wild model or human-agreement evidence, completed VISUS/Hollywood2 empirical validation, or stable API guarantees.

Blended learning has become increasingly important in education reform, especially after the COVID-19 pandemic. This case study examines how rural students perceive blended learning towards education …

Blended learning has become increasingly important in education reform, especially after the COVID-19 pandemic. This case study examines how rural students perceive blended learning towards education reform in Malaysia through a semi-structured interview with 20 students from a selected rural school in Malaysia. Thematic analysis applied to the research found that students had positive views of blended learning, appreciating its flexibility, potential to ensure equitable education, and ability to motivate learning, besides contributing to better educational outcomes and supporting government efforts to reform education. However, students identified several challenges to the implementation of blended learning, such as difficulty focusing on online lessons, unstable internet, and limited comprehension of blended learning. There were mixed responses to the necessity of education reform, with the majority opting for unnecessary or unsure. Hence, this study recommends a versatile learning model integrating online and face-to-face education to better support rural students and ensure an inclusive, equitable, and effective education system.

We present a unified formal theory of intelligence as the progressive minimization of representational residue R = L(S) − K(P) (where K(P) is the Kolmogorov complexity of the invariant…

We present a unified formal theory of intelligence as the progressive minimization of representational residue R = L(S) − K(P) (where K(P) is the Kolmogorov complexity of the invariant structure P and L(S) is the description length of the representation S) subject to maintaining deterministic structural closure C → 1. The generalized cognitive functional G = FV + αR + λ(1 − C) formally unifies Friston’s variational free energy principle with algorithmic complexity theory. From this, we state and prove the Mathematics Attractor Conjecture: sufficiently persistent intelligent systems strictly converge toward axiomatic mathematical representations because such fixed-point geometries simultaneously minimize R and maximize C. Intelligence is not fundamentally about predicting data; it is about the rigorous minimization of representational residue. Systems with high residue will eventually collapse under the weight of their own geometric inconsistencies. By formalizing intelligence as an exact admissibility constraint operating on a finite D = −163 spectral manifold, the DSM-861 architecture provides a mathematical cure for AI hallucination. It successfully replaces the unconstrained, failure-prone continuous sampling methods of legacy systems with rigid, hard-state topological projections, strictly guaranteeing deterministic closure and absolute operational stability for complex systems engineering.

BGC Argo+ v1.0 dataset after revisions. More information can be found at the associated website: BGC Argo+

Nigeria’s electricity sector faces persistent challenges, including insufficient generation capacity, weak transmission and distribution networks, frequent outages, and continued reliance on fos…

Nigeria’s electricity sector faces persistent challenges, including insufficient generation capacity, weak transmission and distribution networks, frequent outages, and continued reliance on fossil-fuel-based power. These limitations constrain economic growth and highlight the need for more resilient, decentralized, and sustainable electricity solutions. This paper presents a systematic review of the integration of micro-grids (MGs) into Nigeria’s power system, focusing on reliability improvement, control strategies, renewable energy integration, and implementation barriers. The review synthesizes literature from major academic databases to examine the current state of Nigeria’s power sector, the drivers for MG deployment, global development trends, and the policy framework supporting adoption. It evaluates the role of MGs in enhancing reliability, voltage support, resilience, rural electrification, and energy access, particularly through the use of renewable energy sources, battery energy storage systems, and intelligent energy management. Key technical issues considered include islanding capability, protection coordination, reliability indices, hierarchical control structures, and both grid-connected and standalone operation within the Nigerian context. The paper’s main contribution is a comprehensive engineering-based assessment of the reliability, control, and integration requirements of MGs in Nigeria, while also identifying major policy, financial, and infrastructural constraints. Overall, the review concludes that MGs offer a practical pathway toward a more reliable, sustainable, and inclusive electricity supply system in Nigeria.

This record provides Version 2.0.1 of the replication package for an instrumented browser-based educational exercise on the one-dimensional cutting-stock problem. The package includes the current Braz…

This record provides Version 2.0.1 of the replication package for an instrumented browser-based educational exercise on the one-dimensional cutting-stock problem. The package includes the current Brazilian Portuguese and English versions of the student instrument, byte-preserved historical versions used during the original study, a deterministic Python and JavaScript verifier, an English class dashboard, the session-log schema and examples, 22 consented session logs without direct personal identifiers, independent human coding, automated tests, reproducibility scripts, and generated results supporting the quantitative claims reported in the associated study. The classroom task uses a fixed cutting-stock instance with 48 pieces and bar capacity 8.5. First Fit Decreasing (FFD) uses 14 bars for this instance, whereas the exact verifier proves an optimum of 13 bars. The replication workflow explicitly distinguishes result fidelity (matches_ffd_result) from procedural fidelity (followed_ffd_procedure and ffd_process_errors). Version 2.0.1 additionally includes a reproducible same-problem transferability validation using a second cutting-stock instance. The second instance has 41 pieces, the same bar capacity of 8.5, and total length 110.5. Its volume lower bound is 13 bars, canonical FFD uses 15 bars, and the exact verifier proves an optimum of 14 bars. The package includes a verified 14-bar witness and an independent certificate explaining why 13 bars are impossible. This validation demonstrates reuse of the same FFD, state-verification, integer-scaling, and exact-search implementation on a structurally different instance of the same problem class; it does not claim demonstrated transferability to a different operations-research problem type. The package also includes an explicit manuscript-claim audit. All entries in the current-manuscript comparison are required to match the reproduced values, including the Figure 2 reference for student 1 (ffd_process_errors = 11 and ffd_process_error_episodes = 4). A separate historical comparison preserves superseded values from the rejected manuscript version solely as archival provenance. At release, all 57 automated tests passed, including JavaScript/Python equivalence, exact-solver, transferability, privacy, and current-manuscript consistency checks. Reproduced outputs match the bundled expected_results.json. The release audit reports 22 session files, 22 unique technical session identifiers, 22 human-coding rows, and no issues under the implemented direct-identifier checks. Source Git commit:5b2f0551ce8bffcbbdf49183f7f02374eb88f223 Archive SHA-256:c2fd392e9f986461f5ccf4cf755513556b9d7e0a83f84378790724a0c2edcef1 The release archive was rebuilt twice from the same source commit and produced the same SHA-256 value. Software components are licensed under the MIT License. Research data and non-software research materials are licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).